A Comparison of Two-Year Outcomes in ZUMA-1 (Axicabtagene Ciloleucel) and SCHOLAR-1 in Patients with Refractory Large B Cell Lymphoma
Notice bibliographique
Résumé
Background: In the SCHOLAR-1 study, the largest published retrospective analysis of outcomes in patients with refractory large B-cell lymphoma (LBCL), the objective response rate (ORR) was 26% and complete response (CR) rate was 7% with available salvage therapies in the pre-chimeric antigen receptor (CAR) T cell therapy era (Crump et al. Blood. 2017). These results served as a benchmark for assessing novel therapies. ZUMA-1 (NCT02348216) is the pivotal, multicenter, single-arm Phase 1/2 study evaluating axicabtagene ciloleucel (axi-cel), an autologous anti-CD19 CAR T cell therapy, in patients with refractory LBCL that supported the approval of axi-cel in the United States and Europe. At a median follow-up of 27.1 months, the ORR in ZUMA-1 was 83% and the CR rate was 58% (Locke et al. Lancet Oncol. 2019). Here, we describe comparative analyses of outcomes in ZUMA-1 and SCHOLAR-1 after adjusting for potential imbalances in refractory status between the 2 studies. Methods: Patients in both studies had refractory LBCL defined as stable disease of ≤ 6 months with ≥ 4 cycles of frontline or ≥ 2 cycles of later-line therapy, progressive disease as best response, or relapse ≤ 12 months post autologous stem cell transplant (SCT). To address potential imbalances between studies in refractory status which could affect response and survival outcomes, standardized analyses were performed which equally weighted the proportions of patients by refractory categorization (primary refractory, refractory to ≥ second-line therapy, or relapse after SCT) and presence of post-refractory SCT in each study. Stratified Cochran-Mantel-Haenszel (CMH) tests and Cox models were used to compare the odds ratio for response and hazard ratio (HR) for survival between ZUMA-1 and SCHOLAR-1. P values were descriptive and were not adjusted for multiplicity. Results: Axi-cel was administered to 101 patients in the pivotal Phase 2 portion of ZUMA-1. In the SCHOLAR-1 analysis, 508 patients were evaluable for response and 497 were evaluable for survival. The median follow-up in ZUMA-1 was 2.3 years, and the median follow-up from the overall SCHOLAR-1 study ranged from 7.6 to 14.8 years across different cohorts. In general, ZUMA-1 patients were more heavily pretreated with more patients receiving ≥ 3 lines of therapy as compared with SCHOLAR-1 (69% vs 23%). ZUMA-1 also had more patients who were refractory to second- or later-line therapy compared with SCHOLAR-1 (76% vs 62%). Fewer patients in ZUMA-1, however, were classified as primary refractory vs those in SCHOLAR-1 (26% vs 45%), and a similar proportion of patients between studies relapsed within 1 year of SCT (21% vs 18%). After standardization, the ORR and CR rate in ZUMA-1 vs SCHOLAR-1 were 72% and 54% vs 22% and 7%, respectively. The odds ratios for ORR and CR rate were 7.2-fold and 11.5-fold higher, respectively in ZUMA-1 vs SCHOLAR-1 (CMH test; P < .0001 for both ORR and CR rate). The 2-year survival rate after standardization was 50% (95% CI, 40% - 59%) in ZUMA-1 and 12% (95% CI, 9% - 15%) in SCHOLAR-1, which translated to a 73% reduction in the risk of death in ZUMA-1 relative to SCHOLAR-1 (HR, 0.27; P < .0001). Conclusions: This standardized analysis of the ZUMA-1 and SCHOLAR-1 studies indicates that treatment with axi-cel in this selected population results in 11.5-fold higher odds of CR and a 73% reduction in the risk of death compared with standard salvage regimens in an unselected population. Although limited by retrospective evaluation and cross-study comparisons, these results support the previous literature indicating that axi-cel is a highly effective treatment option for patients with refractory LBCL. Disclosures Neelapu: Pfizer: Consultancy; BMS: Research Funding; Cellectis: Research Funding; Precision Biosciences: Consultancy; Allogene: Consultancy; Acerta: Research Funding; Cell Medica: Consultancy; Celgene: Consultancy, Research Funding; Karus: Research Funding; Novartis: Consultancy; Poseida: Research Funding; Unum Therapeutics: Consultancy, Research Funding; Incyte: Consultancy; Kite, a Gilead Company: Consultancy, Research Funding; Merck: Consultancy, Research Funding. Locke:Novartis: Other: Scientific Advisor; Kite: Other: Scientific Advisor; Cellular BioMedicine Group Inc.: Consultancy. Bartlett:Genentech, Inc.: Research Funding; Gilead: Research Funding; Autolus: Research Funding; ADC Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Research Funding; Celgene: Research Funding; Forty Seven: Research Funding; Affimed: Research Funding; Immune Design: Research Funding; Incyte: Research Funding; Janssen: Research Funding; Kite Pharma: Research Funding; Merck: Research Funding; Millennium: Research Funding; Pfizer: Research Funding; Pharmacyclics: Research Funding. Reagan:Kite, A Gilead Company: Consultancy; Curis: Consultancy; Seattle Genetics: Research Funding. Miklos:Celgene-Juno: Consultancy; Novartis: Consultancy; Adaptive Biotechnologies: Consultancy, Research Funding; Janssen: Consultancy; Miltenyi: Consultancy, Research Funding; AlloGene: Consultancy; Kite, A Gilead Company: Consultancy, Research Funding; Pharmacyclics: Consultancy, Patents & Royalties, Research Funding; Becton Dickinson: Consultancy; Precision Bioscience: Consultancy. Jacobson:Pfizer: Research Funding; Celgene: Consultancy, Other: travel support; Humanigen: Consultancy, Other: travel support; Precision Biosciences: Consultancy, Other: travel support; Bayer: Consultancy, Other: travel support; Novartis: Consultancy, Honoraria, Other: travel support; Kite, a Gilead Company: Consultancy, Honoraria, Other: travel support. Oluwole:Pfizer: Consultancy; Spectrum: Consultancy; Gilead Sciences: Consultancy; Bayer: Consultancy. Siddiqi:BeiGene: Research Funding; Celgene: Research Funding; TG Therapeutics: Research Funding; Kite, A Gilead Company: Research Funding; Seattle Genetics: Speakers Bureau; Janssen: Speakers Bureau; Juno Therapeutics: Consultancy, Other: travel support, Research Funding; PCYC: Consultancy, Research Funding, Speakers Bureau; AstraZeneca: Consultancy, Research Funding, Speakers Bureau. Crump:Kite/Gilead: Consultancy; F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Servier: Consultancy. Kuruvilla:Seattle Genetics: Honoraria; Roche: Honoraria; Novartis: Honoraria; Merck: Honoraria; Karyopharm: Honoraria; Janssen: Honoraria; Gilead: Honoraria; Celgene: Honoraria; BMS: Honoraria; Astra Zeneca: Honoraria; Amgen: Honoraria; Seattle Genetics: Consultancy; Roche: Consultancy; Merck: Consultancy; Karyopharm: Consultancy; Gilead: Consultancy; Abbvie: Consultancy; BMS: Consultancy; Janssen: Research Funding; Roche: Research Funding. Van Den Neste:Gilead: Other: travel support. Farooq:Celgene: Honoraria; Kite Pharma: Research Funding. Navale:Kite, A Gilead Company: Employment, Equity Ownership; Gilead: Equity Ownership; Allogene: Equity Ownership; Cellectis: Equity Ownership; Bluebird Bio: Equity Ownership; Amgen: Equity Ownership; Organesis: Equity Ownership; Jounce: Equity Ownership; Editas: Equity Ownership; Intellia: Equity Ownership. DePuy:Kite, A Gilead Company: Consultancy, Other: travel support. Kim:Kite, A Gilead Company: Employment.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».